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Biomaterials capable of promoting wound healing and preventing infections remain in great demand to address the global unmet need for the treatment of chronic wounds. Phosphate-based glasses (PG) have shown potential as bioresorbable materials capable of inducing tissue regeneration, while being replaced by regenerated tissue and releasing therapeutic species. In this work, phosphate-glass-based fibers (PGF) in the system P2O5–CaO–Na2O added with 1, 2, 4, 6, and 10 mol % of the therapeutic metallic ions (TMI) Ag+, Zn2+, and Fe3+were manufactured via electrospinning of coacervate gels. Coacervation is a sustainable, cost-effective, water-based method to produce PG. All TMI are effective in promoting wound closure (re-epithelialization) in living human skin ex vivo, where the best-performing system is PGF containing Ag+. In particular, PGF with ≥4 mol % of Ag+is capable of promoting 84% wound closure over 48 h. These results are confirmed by scratch test migration assays, with the PGF-Ag systems containing ≥6 mol % of Ag+, demonstrating significant wound closure enhancement (up to 72%) after 24 h. The PGF-Ag systems are also the most effective in terms of antibacterial activity against both the Gram-positive Staphylococcus aureusand the Gram-negative Escherichia coli. PGF doped with Zn2+shows antibacterial activity only against S. aureusin the systems containing Zn2+≥ 10 mol %. In addition, PGF doped with Fe3+rapidly accelerates ex vivohealing in patient chronic wound skin (>30% in 48 h), demonstrating the utility of doped PGF as a potential therapeutic strategy to treat chronic wounds.

ABSTRACTMultidisciplinary studies try to explain the complexity and the effects of political processes, such as how decision-making, and socio-political transition are connected with urbanism, and activism. Tirana has faced numerous changes over the years, from urbanism, to political landscapes, to economics, to social aspects, and to public spaces within the city (for example, squares and buildings), all of which are key points towards achieving sustainable transformation. This paper tries to explain and understand the political processes and informal placemaking (public space, buildings, activism) following three main perspectives: a) ontologies on political processes approach, the environment, and urban planning; b) activism and protest events; and c) the legal framework on urban planning and activism. Based on previous studies, experiences from the past and these three perspectives, this paper emphasizes the connection between political processes and informal placemaking by answering the questions: “What is the role of urban transformation in activism in Tirana? What are the implications of political processes on activism and urban transformation? The key studies in this paper focus on public spaces around culture heritage buildings in Tirana, such as the National Theatre building and the “Qemal Stafa” National Stadium.

Background: Hereditary hemochromatosis (HH) is an autosomal recessive disorder that leads to iron overload and multiorgan failure. Objectives: The aim of this systematic review was to provide up-to-date evidence of all the current data on the costs and cost effectiveness of screening and treatment for HH. Methods: We searched PubMed, Cochrane Library, National Health Service Economic Evaluation Database (NHSEED), Cost-Effectiveness Analysis Registry (CEA Registry), Health Technology Assessment Database (HTAD), Centre for Reviews and Dissemination (CRD), and Econlit until April 2023 with no date restrictions. Articles that reported cost-utility, cost-description, cost-minimization, cost-effectiveness, or cost-benefit analyses for any kind of management (drugs, screening, etc.) were included in the study. Patients with HH, their siblings, or individuals suspected of having HH were included in the study. All screening and treatment strategies were included. Two authors assessed the quality of evidence related to screening (either phenotype or genotype screening) and treatment (phlebotomy and electrophoresis). Narrative synthesis was used to analyse the similarities and differences between the respective studies. Results: Thirty-nine papers were included in this study. The majority of the studies reported both the cost of phenotype screening, including transferrin saturation (TS), serum ferritin, and liver biopsy, and the cost of genotype screening (HFE screening, C282Y mutation). Few studies reported the cost for phlebotomy and erythrocytapheresis treatment. Data revealed that either phenotype or genotype screening were cost effective compared with no screening. Treatment studies concluded that erythrocytapheresis might be a cost-effective therapy compared with phlebotomy. Conclusions: Economic studies on either the screening, or treatment strategy for HH patients should be performed in more countries. We suggest that cost-effectiveness studies on the role of deferasirox in HH should be carried out as an alternative therapy to phlebotomy.

Prostate cancer is among the most prevalent cancer globally and within Eastern Europe, where there are also higher levels of mortality compared with Western Europe. Cancer control plans exist in most countries in the region. Attention should be given to devising and implementing optimal screening initiatives. Our review has identified that a lack of resources and health system dysfunctions hamper progress in ameliorating the burden of prostate cancer. Regional cooperation is needed as well as drawing on guidelines and findings from elsewhere. Health institutions must also know the latest developments and set up systems that allow swift adoption.

This review explores the effect of common everyday factors, such as alcohol, tea and coffee consumption, on the risk for lung cancer. We performed an umbrella review of current systematic reviews. The risk for lung cancer was increased with alcohol or coffee intake and decreased with tea intake. While evidence for alcohol is of low quality, the effect of coffee may be confounded by the smoking effect. The protective effect of tea intake is present, but the evidence is also of low quality.

This paper presents a framework for training neural network (NN)-based feedback controllers for autonomous agents with deterministic nonlinear dynamics to satisfy task objectives and safety constraints expressed in discrete-time Signal Temporal Logic (DT-STL). Control synthesis that uses the robustness semantics of DT-STL poses challenges due to its non-convexity, non-differentiability, and recursive definition, in particular when it is used to train NN-based controllers. We introduce a smooth neuro-symbolic computation graph to encode DT-STL robustness to represent a smooth approximation of the robustness, enabling the use of powerful stochastic gradient descent and backpropagation-based optimization for training. Our approximation guarantees that it lower bounds the robustness value of a given DT-STL formula, and shows orders of magnitude improvement over existing smooth approximations when applied to control synthesis. We demonstrate our approach on planning to satisfy complex spatio-temporal and sequential tasks, and show scalability with formula complexity.

Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of the DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks (DNNs). However, gathering such data is time-consuming and costly. To address this, we propose an annotation cost-efficient active learning (ANNEAL) method tailored to DML-driven CBIR in RS. ANNEAL aims to create a small but informative training set made up of similar and dissimilar image pairs to be used for accurately learning a metric space. The informativeness of image pairs is evaluated by combining uncertainty and diversity criteria. To assess the uncertainty of image pairs, we introduce two algorithms: 1) metric-guided uncertainty estimation (MGUE) and 2) binary-classifier-guided uncertainty estimation (BCGUE). MGUE algorithm automatically estimates a threshold value that acts as a boundary between similar and dissimilar image pairs based on the distances in the metric space. The closer the similarity between image pairs is to the estimated threshold value, the higher their uncertainty. BCGUE algorithm estimates the uncertainty of the image pairs based on the confidence of the classifier in assigning correct similarity labels. The diversity criterion is assessed through a clustering-based strategy. ANNEAL combines either MGUE or BCGUE algorithm with the clustering-based strategy to select the most informative image pairs, which are then labeled by expert annotators as similar or dissimilar. This way of annotating images significantly reduces the annotation cost compared with annotating images with land-use land-cover class labels. Experimental results on two RS benchmark datasets demonstrate the effectiveness of our method. The code of this work is publicly available at https://git.tu-berlin.de/rsim/anneal_tgrs.

The reduction of pollutant emissions due to the truck operations in a maritime terminal is the objective of this work. The proposed approach is firstly based on a suitable prediction method developed by the authors and aimed at forecasting the number of trucks reaching the terminal to bring export containers in each day of a specified time interval. Once that the curve of truck arrivals is predicted, an emission model is adopted to evaluate the corresponding pollutant emissions. As a result, it is possible to verify in advance whether at certain days the overall emissions overcome a critical threshold. If this happens, it is needed to redistribute the truck arrivals in order to maintain emissions as close as possible to the threshold. This is optimally done by stating and solving an optimization problem whose solutions are tested in the case study of export flows in the PSA Genova Pra’ (PSA GP) terminal.

The objective of this work is to predict the emissions generated by trucks upon their arrival at a port terminal. This prediction is based on a forecast model that predicts truck arrivals, serving as a key input for the proposed methodology. By using the curve of truck arrivals within specific time intervals, an emission model is adopted to estimate the corresponding pollutant emissions. Then, a redistribution algorithm is designed to optimize the scheduling of truck arrivals, effectively mitigating the occurrence of possible peaks in emissions. The algorithm, which takes into account constraints about the truck operations inside the terminal, operates by redistributing the arrival patterns of trucks in a smooth way in order to also consider the truck operators’ reluctance to change the existing schedule. The case study of export flows in the PSA Genova Pra’ (PSA GP) terminal is addressed in the paper.

ObjectivesTo evaluate the efficacy of a new multicomponent, exercise-based injury prevention programme in football players 13–19 years old.MethodsTwo-arm cluster-randomised controlled trial with clubs as the unit of randomisation. 55 football teams from Kosovo of the under 15, under 17 and under 19 age groups were randomly assigned to the intervention (INT; 28 teams) or the control group (CON; 27 teams) and were followed for one football season (August 2021–May 2022). The INT group performed the ‘FUNBALL’ programme after their usual warm-up at least twice per week, while the CON group followed their usual training routine. The primary outcome measure was the overall number of football-related injuries. Secondary outcomes were region-specific injuries of the lower limbs (hip/groin, thigh, knee, lower leg, ankle and foot) and injury severity.Results319 injuries occurred, 132 in the INT and 187 in the CON group. The INT group used the ‘FUNBALL’ programme in 72.2% of all training sessions, on average 2.2 times per week. There was a significantly lower incidence in the INT group regarding the overall number of injuries (incidence rate ratio (IRR) 0.69, 95% CI 0.55 to 0.87), the number of thigh injuries (IRR 0.62, 95% CI 0.39 to 0.98), of moderate (time loss between 7 and 28 days) (IRR 0.65, 95% CI 0.44 to 0.97) and of severe injuries (time loss >28 days) (IRR 0.51, 95% CI 0.28 to 0.91).ConclusionThe ‘FUNBALL’ programme reduced the incidence of football-related injuries among male adolescent football players, and its regular use for injury prevention in this population is recommended.Trial registration numberNCT05137015.

Normal aging is accompanied by broad loss of cognitive function in humans and rodents, including declines in cognitive flexibility. In extinction, a conditional stimulus (CS) that was previously paired with a footshock is presented alone. This procedure reliably reduces conditional freezing behavior in young adult rats. Here, we aimed to investigate how normal aging affects extinction learning. Using young (3 months) and aged (20 months) male and female Long Evans rats, we compared extinction (using 20 CS-alone presentations) to a no extinction control (equal exposure to the conditioning chamber without CS presentations) following delay fear conditioning. We found that young animals in the extinction group showed a decrease in freezing following extinction; aged animals did not. We next examined changes in neural activity using expression of the immediate early gene zif268. In young animals, extinction corresponded with decreased expression of zif268 in the basolateral amygdala and anterior retrosplenial cortex; this was not observed in aged animals. Further, aged animals showed increased zif268 expression in each region examined, suggesting that dysfunction in neural activity precedes cognitive deficits. These results demonstrate that aging impacts both extinction learning and neural activity.

Membranous nephropathy (MN) is a pattern of injury caused by autoantibodies binding to specific target antigens, with accumulation of immune complexes along the subepithelial region of glomerular basement membranes. The past 20 years have brought revolutionary advances in the understanding of MN, particularly via the discovery of novel target antigens and their respective autoantibodies. These discoveries have challenged the traditional classification of MN into primary and secondary forms. At least 14 target antigens have been identified, accounting for 80%–90% of cases of MN. Many of the forms of MN associated with these novel MN target antigens have distinctive clinical and pathologic phenotypes. The Mayo Clinic consensus report on MN proposes a 2-step classification of MN. The first step, when possible, is identification of the target antigen, based on a multistep algorithm and using a combination of serology, staining of the kidney biopsy tissue by immunofluorescence or immunohistochemistry, and/or mass spectrometry methodology. The second step is the search for a potential underlying disease or associated condition, which is particularly relevant when knowledge of the target antigen is available to direct it. The meeting acknowledges that the resources and equipment required to perform the proposed testing may not be generally available. However, the meeting consensus was that the time has come to adopt an antigen-based classification of MN because this approach will allow for accurate and specific MN diagnosis, with significant implications for patient management and targeted treatment.

Membranous nephropathy (MN) is a pattern of injury caused by autoantibodies binding to specific target antigens, with accumulation of immune complexes along the subepithelial region of glomerular basement membranes. The past 20 years have brought revolutionary advances in the understanding of MN, particularly via the discovery of novel target antigens and their respective autoantibodies. These discoveries have challenged the traditional classification of MN into primary and secondary forms. At least 14 target antigens have been identified, accounting for 80%–90% of cases of MN. Many of the forms of MN associated with these novel MN target antigens have distinctive clinical and pathologic phenotypes. The Mayo Clinic consensus report on MN proposes a 2-step classification of MN. The first step, when possible, is identification of the target antigen, based on a multistep algorithm and using a combination of serology, staining of the kidney biopsy tissue by immunofluorescence or immunohistochemistry, and/or mass spectrometry methodology. The second step is the search for a potential underlying disease or associated condition, which is particularly relevant when knowledge of the target antigen is available to direct it. The meeting acknowledges that the resources and equipment required to perform the proposed testing may not be generally available. However, the meeting consensus was that the time has come to adopt an antigen-based classification of MN because this approach will allow for accurate and specific MN diagnosis, with significant implications for patient management and targeted treatment.

One of the key factors influencing agricultural development and farm performance is access to finance. There are several factors that influence access to finance, including financial management capacity, which is linked to financial literacy. The paper analyzes the factors which determine farmers’ financial management behavior, in the case of Kosovo, based on a structured farm survey focusing on the vineyard sector. The findings suggest that half of the interviewed farmers did not keep records regarding costs and incomes. The likelihood to keep financial records is linked to several household and farm attributes. Market-oriented farmers who had contracts were more likely to keep financial records than those who did not. Policymakers through public advisory services as well as financial institutions should address the need to improve financial literacy among farmers.

AbstractKillings, as the most extreme form of violence against journalists, receive considerable attention, but journalists experience a variety of threats from surveillance to gendered cyber targeting and hate speech, or even the intentional deprivation of their financial basis. This article provides a comprehensive, interdisciplinary framework of journalists’ safety, summarized in a conceptual model. The aim is to advance the study of journalists’ safety and improve safety practices, journalism education, advocacy, and policy making - vital as press freedom and fundamental human rights face multifaceted challenges, compromising journalists’ ability to serve their societies. Journalists’ occupational safety comprises personal (physical, psychological) and infrastructural (digital, financial) dimensions. Safety can be objective and subjective by operating on material and perceptional levels. It is moderated by individual (micro), organizational/institutional (meso), and systemic (macro) risk factors, rooted in power dynamics defining boundaries for journalists’ work, which, if crossed, result in threats and create work-related stress. Stress requires coping, ideally resulting in resilience and resistance, and manifested in journalists’ continued role performance with autonomy. Compromised safety has personal and social consequences as threats might affect role performance and even lead to an exit from the profession, thus also affecting journalism’s wider function as a key institution.

The discovery of phospholipase A2 receptor 1 as the major target antigen in membranous nephropathy (MN) has initiated a decade of major advances in the understanding of MN pathophysiology and improvement of patient care. In this Issue, Zhang et al.identified potential T-cell epitopes of phospholipase A2 receptor 1 in patients with MN. The characterization of the pathogenic T- and B-cell epitopes on phospholipase A2 receptor 1 is an important step moving from the current unspecific immunosuppressive therapies toward antigen-specific MN treatments.

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Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility pipes) and reconstruct the underground scene. There are two challenges for GPR-based inspection, which are GPR data collection and 3D subsurface object imaging. To address these challenges, we first propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose with GPR measurement in real-time. Moreover, to improve the 3D GPR imaging, we introduce a learning-based GPR data analysis method, which includes a noise removal module to clear the background noise in raw GPR data and a Convolutional Recurrent Neural Network (CRNN) to estimate the dielectric value of subsurface medium in each GPR B-scan data. We use both field and synthetic data to verify the proposed methods. Experimental results demonstrate that our proposed methods can achieve higher performance and faster processing speed in 3D GPR imaging than baseline methods.

The marbled polecat (Vormela peregusna) is a small mustelid that occurs from the Balkans to Mongolia and is listed as vulnerable under the International Union for Conservation of Nature's (IUCN) Red List. There are currently no efficient methods to monitor populations at a broad scale and most records come from opportunistic sightings. However, the elusive nature and unique pelage of the species often results in a lot of interest when sighted, with observations regularly being shared on social media platforms. Such records from social media can provide an extensive source of freely available information that could be used to inform the species’ distribution. In this study, we systematically collected marbled polecat records from five social media platforms by using a manual and automated search targeting the western range of the species. We identified 131 unique marbled polecat sightings originating mostly from Facebook, Instagram and Twitter. The records confirmed the species’ presence in 92 50-km grid cells within the study area and outperformed other sources, such as GBIF and scientific literature searches. The combination of all three datasets resulted in 133 presence points, which was sufficient enough to perform further habitat suitability modelling and reliable alpha hull range estimates. The social media search was well suited to clarify broad distribution patterns of marbled polecat, but did not detect the species in areas where its presence was most uncertain. The results of the modelling work, however, can be used to target further dedicated survey work for the species. The framework used in this study can be applied to provide more detailed information on distribution and occurrence patterns for other rare or under-studied species.

Image captioning (IC) systems are generally based on encoder–decoder architecture where convolutional neural networks (CNNs) are employed to represent an image with discriminative features and recurrent neural networks (RNNs) sequentially generate a sentence description. Even though a lot of effort has been devoted lately to designing reliable IC systems, the task is far from being solved. The generated descriptions can be affected by different errors related to the attributes and the objects present in the scene. Moreover, once an error occurs, it can be propagated in the recurrent layers of the decoder generating non-accurate descriptions. To solve this problem, we propose two postprocessing strategies applied to the generated descriptions to rectify the errors and improve their quality. The proposed postprocessing strategies are based on hidden Markov models (HMMs) and Viterbi algorithm. The proposed postprocessing strategies can be applied to any encoder–decoder IC system. They are applied at test time once the IC system is trained. In particular, we propose to rectify a sentence once it is fully generated (post-generation strategy) or at each time instant of the generation process (in-generation strategy). Experiments conducted on four different IC datasets confirm the promising capabilities of the proposed postprocessing strategies to rectify the output of a simple encoder–decoder by generating more coherent descriptions. The achieved results are competitive and sometimes better than complex IC systems.

Given the variable nature of wind speed and the importance of accurately determining the energy that can be generated at a given site, understanding the wind speed at different time scales is crucial. In addition to differences within a very short period (i.e., hourly and daily), these changes are also pronounced throughout the seasons. They are affected by the atmospheric conditions and the terrain's complexity. Therefore, this study investigates the seasonal wind speed variability and its impact on the potential energy generation in a representative study case of Koznica, the mountainous region in Kosovo. The wind speed measurements campaign started in May 2019 and ended in April 2020; the measurements were made at a 10 min time scale. Ground measurements show that the wind direction is mainly northwest and southeast. Then, the wind speed and potential energy generation variability analysis were conducted for three different measurement heights. The results show that winter and spring have the highest potential wind energy capacity with an average speed of 6.7 m/s. In comparison, the average wind speed is 6.12 m/s. Potential energy generation for each season (i.e., spring, summer, autumn, and winter is as follows: 64,396.7, 22,040.3, 42,539.3, and 46,417.2 MWh/year, respectively, while the average capacity factor is 25%. Solution-oriented findings from this study might provide valuable insights to policymakers and investors regarding wind power energy exploration in Kosovo and other places with similar geo-climatic conditions.

Membranous nephropathy (MN) is an autoimmune disease caused by circulating antibodies, which target antigens on podocyte surfaces. We identified NTNG1 as a membrane protein that podocytes express and that circulating (mainly IgG4-subclass) autoantibodies target in patients with primary MN. Staining the target antigen in the biopsy and measuring circulating antibodies to determine the immunologic activity of disease provide a molecular characterization that improves diagnosis and clinical management of MN.

Background: Routine health information systems (RHISs) are crucial to informing decision-making at all levels of the health system. However, the use of RHIS data in low- and middle-income countries (LMICs) is limited due to concerns regarding quality, accuracy, timeliness, completeness and representativeness.Objective: This study systematically reviewed technical, behavioural and organisational/environmental challenges that hinder the use of RHIS data in LMICs and strategies implemented to overcome these challenges.Method: Four electronic databases were searched for studies describing challenges associated with the use of RHIS data and/or strategies implemented to circumvent these challenges in LMICs. Identified articles were screened against inclusion and exclusion criteria by two independent reviewers.Results: Sixty studies met the inclusion criteria and were included in this review, 55 of which described challenges in using RHIS data and 20 of which focused on strategies to address these challenges. Identified challenges and strategies were organised by their technical, behavioural and organisational/environmental determinants and by the core steps of the data process. Organisational/environmental challenges were the most commonly reported barriers to data use, while technical challenges were the most commonly addressed with strategies.Conclusion: Despite the known benefits of RHIS data for health system strengthening, numerous challenges continue to impede their use in practice.Implications: Additional research is needed to identify effective strategies for addressing the determinants of RHIS use, particularly given the disconnect identified between the type of challenge most commonly described in the literature and the type of challenge most commonly targeted for interventions.